Protein sequence design with deep generative models

TL;DR

This paper reviews recent advances in using deep generative machine learning models for protein sequence design, emphasizing how these methods can accelerate protein engineering by leveraging prior knowledge.

Contribution

It provides a comprehensive overview of the emerging field of deep generative models applied to protein sequence generation, highlighting recent developments and applications.

Findings

Deep generative models are increasingly used for protein sequence design.

Machine learning accelerates protein engineering by generating diverse sequences.

Recent applications demonstrate improved protein property optimization.

Abstract

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.